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Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization
Yue Wang
, Lan Luo
, Matthew T. Freedman
,
Sun Yuan Kung
Electrical and Computer Engineering
Center for Statistics & Machine Learning
Research output
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Contribution to journal
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Article
›
peer-review
58
Scopus citations
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Dive into the research topics of 'Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization'. Together they form a unique fingerprint.
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Keyphrases
Breast Cancer Detection
50%
Computer-aided Diagnosis (CADx)
50%
Data Knowledge
50%
Digital Mammogram
50%
Finite Mixture Model
100%
Finite Normal Mixtures
50%
Hierarchical Visualization
50%
Information Theoretic Criteria
50%
Knowledge Discovery
50%
Multimodal Data
50%
Multivariate Data Mining
50%
Principal Components Artificial Neural Networks (PC-ANN)
50%
Probabilistic Principal Components
100%
Top Level
50%
Visual Explanation
50%
Visual Exploration
50%
Visualization Algorithms
100%
Mathematics
Complete Data
33%
Data Visualization
100%
Finite Mixture Model
100%
Multimodal Data
33%
Computer Science
Aided Diagnosis
33%
Data Mining
33%
Knowledge Discovery
33%
Multivariate Data
33%
Visual Exploration
33%